


'''simple docstring'''


from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast
from ...utils import logging


UpperCAmelCase_						:  List[Any]				  =						logging.get_logger(__name__)

UpperCAmelCase_						:  int				  =						{
    'EleutherAI/gpt-neo-1.3B': 'https://huggingface.co/EleutherAI/gpt-neo-1.3B/resolve/main/config.json',
    # See all GPTNeo models at https://huggingface.co/models?filter=gpt_neo
}





class        lowercase__    (		_snake_case   ):





							'''simple docstring'''


							A_				: Dict       =    """gpt_neo"""
							A_				: int       =    ["""past_key_values"""]
							A_				: Dict       =    {"""num_attention_heads""": """num_heads""", """num_hidden_layers""": """num_layers"""}






							def __init__(       self       ,   __snake_case=5_0257       ,   __snake_case=2048       ,   __snake_case=2048       ,   __snake_case=24       ,   __snake_case=[[["global", "local"], 12]]       ,   __snake_case=16       ,   __snake_case=None       ,   __snake_case=256       ,   __snake_case="gelu_new"       ,   __snake_case=0.0       ,   __snake_case=0.0       ,   __snake_case=0.0       ,   __snake_case=0.1       ,   __snake_case=1e-5       ,   __snake_case=0.02       ,   __snake_case=True       ,   __snake_case=5_0256       ,   __snake_case=5_0256       ,   **__snake_case       ,   ):
											_SCREAMING_SNAKE_CASE					:			List[str]							= vocab_size
											_SCREAMING_SNAKE_CASE					:			Optional[Any]							= max_position_embeddings
											_SCREAMING_SNAKE_CASE					:			List[str]							= hidden_size
											_SCREAMING_SNAKE_CASE					:			Dict							= num_layers
											_SCREAMING_SNAKE_CASE					:			Any							= num_heads
											_SCREAMING_SNAKE_CASE					:			str							= intermediate_size
											_SCREAMING_SNAKE_CASE					:			Tuple							= window_size
											_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= activation_function
											_SCREAMING_SNAKE_CASE					:			Dict							= resid_dropout
											_SCREAMING_SNAKE_CASE					:			Optional[int]							= embed_dropout
											_SCREAMING_SNAKE_CASE					:			str							= attention_dropout
											_SCREAMING_SNAKE_CASE					:			List[str]							= classifier_dropout
											_SCREAMING_SNAKE_CASE					:			Optional[int]							= layer_norm_epsilon
											_SCREAMING_SNAKE_CASE					:			str							= initializer_range
											_SCREAMING_SNAKE_CASE					:			str							= use_cache

											_SCREAMING_SNAKE_CASE					:			Optional[Any]							= bos_token_id
											_SCREAMING_SNAKE_CASE					:			List[Any]							= eos_token_id

											_SCREAMING_SNAKE_CASE					:			int							= attention_types
											_SCREAMING_SNAKE_CASE					:			Dict							= self.expand_attention_types_params(__snake_case    )

											if len(self.attention_layers    ) != self.num_layers:
															raise ValueError(
															    """Configuration for convolutional module is incorrect. """
															    """It is required that `len(config.attention_layers)` == `config.num_layers` """
															    f"""but is `len(config.attention_layers) = {len(self.attention_layers    )}`, """
															    f"""`config.num_layers = {self.num_layers}`. """
															    """`config.attention_layers` is prepared using `config.attention_types`. """
															    """Please verify the value of `config.attention_types` argument."""    )

											super().__init__(bos_token_id=__snake_case       ,   eos_token_id=__snake_case       ,   **__snake_case    )






							@staticmethod
							def UpperCAmelCase_      (       __snake_case    ):
											_SCREAMING_SNAKE_CASE					:			Any							= []
											for item in attention_types:
															for _ in range(item[1]    ):
																			attentions.extend(item[0]    )
											return attentions







def      snake_case_  (    SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__	):







				"""simple docstring"""





				import torch

				_SCREAMING_SNAKE_CASE					:			Any							= input.size()
				_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= len(SCREAMING_SNAKE_CASE__	)
				_SCREAMING_SNAKE_CASE					:			Optional[Any]							= shape[dimension]

				_SCREAMING_SNAKE_CASE					:			str							= torch.arange(0				, SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__	)
				_SCREAMING_SNAKE_CASE					:			Any							= torch.div(sizedim - size				, SCREAMING_SNAKE_CASE__				, rounding_mode="""floor"""	) + 1
				_SCREAMING_SNAKE_CASE					:			Dict							= torch.arange(SCREAMING_SNAKE_CASE__	) + low_indices[:min_length][:, None]

				_SCREAMING_SNAKE_CASE					:			Optional[Any]							= [slice(SCREAMING_SNAKE_CASE__	)] * rank
				_SCREAMING_SNAKE_CASE					:			Optional[int]							= indices
				_SCREAMING_SNAKE_CASE					:			List[str]							= input[s]

				_SCREAMING_SNAKE_CASE					:			List[Any]							= list(range(0				, rank + 1	)	)
				perm.append(perm.pop(dimension + 1	)	)

				return sliced.permute(SCREAMING_SNAKE_CASE__	)







def      snake_case_  (    SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__	):







				"""simple docstring"""





				import torch

				_SCREAMING_SNAKE_CASE					:			Dict							= torch.arange(1				, SCREAMING_SNAKE_CASE__	)
				_SCREAMING_SNAKE_CASE					:			int							= torch.remainder(SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__	)
				_SCREAMING_SNAKE_CASE					:			Optional[Any]							= remainders == 0
				_SCREAMING_SNAKE_CASE					:			str							= candidates[divisor_indices]
				_SCREAMING_SNAKE_CASE					:			List[Any]							= torch.max(SCREAMING_SNAKE_CASE__	)
				return largest_divisor, torch.div(SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__				, rounding_mode="""floor"""	)







class        lowercase__    (		_snake_case   ):





							'''simple docstring'''


							@property
							def UpperCAmelCase_      (       self    ):
											_SCREAMING_SNAKE_CASE					:			Dict							= OrderedDict({"""input_ids""": {0: """batch""", 1: """sequence"""}}    )
											if self.use_past:
															self.fill_with_past_key_values_(__snake_case       ,   direction="""inputs"""    )
															_SCREAMING_SNAKE_CASE					:			Tuple							= {0: """batch""", 1: """past_sequence + sequence"""}
											else:
															_SCREAMING_SNAKE_CASE					:			Dict							= {0: """batch""", 1: """sequence"""}

											return common_inputs






							@property
							def UpperCAmelCase_      (       self    ):
											return self._config.num_heads






							def UpperCAmelCase_      (       self       ,   __snake_case       ,   __snake_case = -1       ,   __snake_case = -1       ,   __snake_case = False       ,   __snake_case = None       ,   ):
											_SCREAMING_SNAKE_CASE					:			Optional[int]							= super(__snake_case       ,   self    ).generate_dummy_inputs(
											    __snake_case       ,   batch_size=__snake_case       ,   seq_length=__snake_case       ,   is_pair=__snake_case       ,   framework=__snake_case    )

											# We need to order the input in the way they appears in the forward()
											_SCREAMING_SNAKE_CASE					:			str							= OrderedDict({"""input_ids""": common_inputs["""input_ids"""]}    )

											# Need to add the past_keys
											if self.use_past:
															if not is_torch_available():
																			raise ValueError("""Cannot generate dummy past_keys inputs without PyTorch installed."""    )
															else:
																			import torch

																			_SCREAMING_SNAKE_CASE						,			_SCREAMING_SNAKE_CASE					:			List[str]							= common_inputs["""input_ids"""].shape
																			# Not using the same length for past_key_values
																			_SCREAMING_SNAKE_CASE					:			Optional[Any]							= seqlen + 2
																			_SCREAMING_SNAKE_CASE					:			Optional[int]							= (
																			    batch,
																			    self.num_attention_heads,
																			    past_key_values_length,
																			    self._config.hidden_size // self.num_attention_heads,
																			)
																			_SCREAMING_SNAKE_CASE					:			str							= [
																			    (torch.zeros(__snake_case    ), torch.zeros(__snake_case    )) for _ in range(self.num_layers    )
																			]

											_SCREAMING_SNAKE_CASE					:			List[Any]							= common_inputs["""attention_mask"""]
											if self.use_past:
															_SCREAMING_SNAKE_CASE					:			Dict							= ordered_inputs["""attention_mask"""].dtype
															_SCREAMING_SNAKE_CASE					:			Dict							= torch.cat(
															    [ordered_inputs["""attention_mask"""], torch.ones(__snake_case       ,   __snake_case       ,   dtype=__snake_case    )]       ,   dim=1    )

											return ordered_inputs






							@property
							def UpperCAmelCase_      (       self    ):
											return 13








'''simple docstring'''


import tempfile
import unittest

from make_student import create_student_by_copying_alternating_layers

from transformers import AutoConfig
from transformers.file_utils import cached_property
from transformers.testing_utils import require_torch


UpperCAmelCase_						:  List[Any]				  =						'sshleifer/bart-tiny-random'
UpperCAmelCase_						:  Tuple				  =						'patrickvonplaten/t5-tiny-random'





@require_torch
class        lowercase__    (		unittest.TestCase   ):





							'''simple docstring'''


							@cached_property
							def UpperCAmelCase_      (       self    ):
											return AutoConfig.from_pretrained(__snake_case    )






							def UpperCAmelCase_      (       self    ):
											_SCREAMING_SNAKE_CASE						,			*_SCREAMING_SNAKE_CASE					:			int							= create_student_by_copying_alternating_layers(__snake_case       ,   tempfile.mkdtemp()       ,   e=1       ,   d=1    )
											self.assertEqual(student.config.num_hidden_layers       ,   1    )






							def UpperCAmelCase_      (       self    ):
											_SCREAMING_SNAKE_CASE						,			*_SCREAMING_SNAKE_CASE					:			int							= create_student_by_copying_alternating_layers(__snake_case       ,   tempfile.mkdtemp()       ,   e=1       ,   d=__snake_case    )






							def UpperCAmelCase_      (       self    ):
											_SCREAMING_SNAKE_CASE						,			*_SCREAMING_SNAKE_CASE					:			str							= create_student_by_copying_alternating_layers(__snake_case       ,   tempfile.mkdtemp()       ,   e=1       ,   d=__snake_case    )
											self.assertEqual(student.config.encoder_layers       ,   1    )
											self.assertEqual(student.config.decoder_layers       ,   self.teacher_config.encoder_layers    )






							def UpperCAmelCase_      (       self    ):
											_SCREAMING_SNAKE_CASE						,			*_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= create_student_by_copying_alternating_layers(__snake_case       ,   tempfile.mkdtemp()       ,   e=1       ,   d=1    )
											self.assertEqual(student.config.encoder_layers       ,   1    )
											self.assertEqual(student.config.decoder_layers       ,   1    )






							def UpperCAmelCase_      (       self    ):
											with self.assertRaises(__snake_case    ):
															create_student_by_copying_alternating_layers(__snake_case       ,   tempfile.mkdtemp()       ,   e=__snake_case       ,   d=__snake_case    )




